@inproceedings{1818eab9a574402fa0978d0301004ae0,
title = "Pattern discovery from audio recordings by Variable Markov Oracle: A music information dynamics approach",
abstract = "In this paper, a framework for automatic pattern discovery within an audio recording is proposed. The concept of the proposed framework stems from music information dynamics and is realized by Variable Markov Oracle. Music information dynamics is the research area focusing on information theoretic measures describing musical structure and is thus closely related to the field of music pattern discovery. Variable Markov Oracle is a data structure that provides both fast retrieval of repeated sub-clips from a signal and efficient calculation of music information dynamics measures. Evaluation of the proposed framework is performed on the JKU Patterns Development Dataset with significantly improved performance of the current state of the art.",
keywords = "Data structures, Music information retrieval, Pattern analysis, Variable Markov Oracle",
author = "Wang, {Cheng I.} and Shlomo Dubnov",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 ; Conference date: 19-04-2014 Through 24-04-2014",
year = "2015",
month = aug,
day = "4",
doi = "10.1109/ICASSP.2015.7178056",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "683--687",
booktitle = "2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings",
address = "United States",
}